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Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

Author

Listed:
  • Yilan Luo

    (Department of Mathematics and Statistics, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
    These authors contributed equally to this work.)

  • Deniz Sezer

    (Department of Mathematics and Statistics, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
    These authors contributed equally to this work.)

  • David Wood

    (Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
    These authors contributed equally to this work.)

  • Mingkuan Wu

    (Department of Mathematics and Statistics, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
    These authors contributed equally to this work.)

  • Hamid Zareipour

    (Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
    These authors contributed equally to this work.)

Abstract

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

Suggested Citation

  • Yilan Luo & Deniz Sezer & David Wood & Mingkuan Wu & Hamid Zareipour, 2019. "Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada," Energies, MDPI, vol. 12(10), pages 1-29, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1998-:d:234127
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    References listed on IDEAS

    as
    1. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    2. David J. Allcroft & Chris A. Glasbey, 2003. "A latent Gaussian Markov random‐field model for spatiotemporal rainfall disaggregation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 487-498, October.
    3. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    4. Caroline De Oliveira Costa Souza Rosa & Kelly Alonso Costa & Eliane Da Silva Christo & Pâmela Braga Bertahone, 2017. "Complementarity of Hydro, Photovoltaic, and Wind Power in Rio de Janeiro State," Sustainability, MDPI, vol. 9(7), pages 1-12, June.
    5. Jurasz, Jakub & Beluco, Alexandre & Canales, Fausto A., 2018. "The impact of complementarity on power supply reliability of small scale hybrid energy systems," Energy, Elsevier, vol. 161(C), pages 737-743.
    6. Gneiting T., 2002. "Nonseparable, Stationary Covariance Functions for Space-Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 590-600, June.
    7. Hering, Amanda S. & Genton, Marc G., 2010. "Powering Up With Space-Time Wind Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 92-104.
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